BACKGROUND: The diagnosis of peripheral arterial disease (PAD) is commonly applied for symptoms related to atherosclerotic obstructions in the lower extremity, though its clinical manifestations range from an abnormal ankle-brachial index to critical limb ischemia. Subsequently, management and prognosis of PAD vary widely with the disease stage. A critical aspect is how this variation is addressed in administrative database-based studies that rely on diagnosis codes for case identification. The objective of this scoping review is to inventory the identification strategies used in studies on PAD that rely on administrative databases, to map the pros and cons of the International Classification of Diseases (ICD) codes applied, and to propose a first outline for a consensus framework for case identification in administrative databases.
METHODS: Registry-based reports published between 2010 and 2021 were identified through a systematic PubMed search. Studies were subcategorized on the basis of the expressed study focus: claudication, critical limb ischemia, or general peripheral arterial disease, and the ICD code(s) applied for case identification mapped.
RESULTS: Ninety studies were identified, of which 36 (40%) did not specify the grade of PAD studied. Forty-nine (54%) articles specified PAD grade studied. Five (6%) articles specified different PAD subgroups in methods and baseline demographics, but not in further analyses. Mapping of the ICD codes applied for case identification for studies that specified the PAD grade studied indicated a remarkable heterogeneity, overlap, and inconsistency.
CONCLUSIONS: A large proportion of registry-based studies on PAD fail to define the study focus. In addition, inconsistent strategies are used for PAD case identification in studies that report a focus. These findings challenge study validity and interfere with inter-study comparison. This scoping review provides a first initiative for a consensus framework for standardized case selection in administrative studies on PAD. It is anticipated that more uniform coding will improve study validity and facilitate inter-study comparisons.

Immortal time bias is a well-recognized bias in clinical epidemiology but is rarely discussed in environmental epidemiology. Under the target trial framework, this bias is formally conceptualized as a misalignment between the start of study follow-up (time 0) and treatment assignment. This misalignment can occur when attained duration of follow-up is encoded into treatment assignment using minimums, maximums, or averages. The bias can be exacerbated in the presence of time trends commonly found in environmental exposures. Using lung cancer cases from the California Cancer Registry (2000-2010) linked with estimated concentrations of particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM2.5), we replicated previous studies that averaged PM2.5 exposure over follow-up in a time-to-event model. We compared this approach with one that ensures alignment between time 0 and treatment assignment, a discrete-time approach. In the former approach, the estimated overall hazard ratio for a 5-μg/m3 increase in PM2.5 was 1.38 (95% confidence interval: 1.36, 1.40). Under the discrete-time approach, the estimated pooled odds ratio was 0.99 (95% confidence interval: 0.98, 1.00). We conclude that the strong estimated effect in the former approach was likely driven by immortal time bias, due to misalignment at time 0. Our findings highlight the importance of appropriately conceptualizing a time-varying environmental exposure under the target trial framework to avoid introducing preventable systematic errors.

OBJECTIVE: Randomized controlled trials (RCTs) are the preferred source of evidence for the relative effect of healthcare interventions summarized in knowledge syntheses. Nonrandomized studies of interventions (NRSI) may provide replacement, sequential, or complementary evidence to RCTs. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach can provide different options for properly using RCTs and NRSI integrated in health syntheses. In this article, we discuss different implications on the certainty of evidence when authors consider the use of NRSI and RCTs in systematic reviews using GRADE. Although this is a GRADE-related article, it is not an official GRADE guidance or concept article.
METHODS: We present case studies used during GRADE working group meetings for discussion of the effects of using NRSI and RCTs on GRADE domains and on the certainty of evidence. Several concepts were discussed through iterative feedback with experts in GRADE methods and Cochrane authors. We compared suggested solutions for possible scenarios that can be met in evidence syntheses informing decisions and future guidance.
RESULTS: Different scenarios for the use of RCTs and NRSI in evidence syntheses are presented, focusing on how different GRADE ratings between RCTs and NRSI affect the overall assessment of the evidence and possible health recommendations.
CONCLUSIONS: Considering differences and similarities grounded in the GRADE approach between NRSI and RCTs may help complement one another and maximize the value of knowledge syntheses and health recommendations.

In multiply matched case-control studies, a number of cases and controls may be included in each matched set. However, when per-participant costs between cases and controls differ, investigators should be aware of how the numbers of cases and controls per matched set affect the overall total study cost. Traditional statistical approaches to designing case-control studies do not account for study costs. Given an effect size, the power to detect differences is typically a function of the numbers of cases and controls within each matched set. Therefore, the same level of statistical power will be achieved based on various combinations of the numbers of cases and controls. Typical matched case-control studies match a case to a number of controls by levels of 1 or more known factors. Several authors have shown that for study designs with 1 case per matched set, the optimal number of controls within each matched set that minimizes the total study cost is the square root of the ratio of the cost of a case to the cost of a control. Herein, we extend this result to the setting of a multiply matched case-control study design, when 1 or more cases are matched to controls within each matched set. A Shiny web application implementation of the proposed methods is presented.

Intensive case management (ICM) programmes for psychotic patients are effective in improving outcomes, but often unfeasible in resource-poor settings, as they typically require extensive human resources and expertise. We developed and evaluated the effectiveness of a less intensive case management program (LICM), led by community health workers, on one-year social functioning and service use.
A prospective cohort study was conducted on patients aged 18 and above residing in a hospital catchment area. Outcomes were compared between LICM (n = 64) and non-LICM participants (n = 485). A counterfactual framework approach was applied to assess causal effects of the LICM on outcomes. The programme effectiveness was analyzed by augmented-inverse probability of treatment weighting (AIPW) to estimate potential outcome mean (POM) and average treatment effect (ATE). Outcomes were employment status and use of emergency, inpatient and outpatient services. Analyses were stratified by the number of previous psychotic relapse (≤ 1, > 1) to assess heterogeneity of treatment effect on those in an early and later stages of psychotic illness.
In the early-stage cohort, the likelihood of being employed at one year post-baseline was significantly greater in LICM participants than non-LICM participants (ATE 0.10, 95%CI 0.05-0.14, p < 0.001), whereas service use of all types, except outpatient, was not significantly different between the two groups. In the later-stage cohort, the likelihoods of employment between the two groups at post-baseline were similar (ATE -0.02, 95%CI -0.19-0.15, p = 0.826), whereas service use of all types was significantly higher in LICM participants.
LICM in a setting where community mental services are scarce may benefit those at an early stage of psychotic illness, by leading to better social functioning and no higher use of unscheduled services at the end of the programme, possibly through their better prognosis and medication adherence. A more intensive case management model may be appropriate for those in a later stage of the illness.

Individuals may respond differently to the same treatment, and there is a need to understand such heterogeneity of causal individual treatment effects. We propose and evaluate a modelling approach to better understand this heterogeneity from observational studies by identifying patient subgroups with a markedly deviating response to treatment. We illustrate this approach in a primary care case-study of antibiotic (AB) prescription on recovery from acute rhino-sinusitis (ARS).
Our approach consists of four stages and is applied to a large dataset in primary care dataset of 24,392 patients suspected of suffering from ARS. We first identify pre-treatment variables that either confound the relationship between treatment and outcome or are risk factors of the outcome. Second, based on the pre-treatment variables we create Synthetic Random Forest (SRF) models to compute the potential outcomes and subsequently the causal individual treatment effect (ITE) estimates. Third, we perform subgroup discovery using the ITE estimates as outcomes to identify positive and negative responders. Fourth, we evaluate the predictive performance of the identified subgroups for predicting the outcome in two ways: the likelihood ratio test, and whether the subgroups are selected via the Akaike Information Criterion (AIC) using backward stepwise variable selection. We validate the whole modelling strategy by means of 10-fold-cross-validation.
Based on 20 pre-treatment variables, four subgroups (three for positive responders and one for negative responders) were identified. The log likelihood ratio tests showed that the subgroups were significant. Variable selection using the AIC kept two of the four subgroups, one for positive responders and one for negative responders. As for the validation of the whole modelling strategy, all reported measures (the number of pre-treatment variables associated with the outcome, number of subgroups, number of subgroups surviving variable selection and coverage) showed little variation.
With the proposed approach, we identified subgroups of positive and negative responders to treatment that markedly deviate from the mean response. The subgroups showed additive predictive value of the outcome. The modelling approach strategy was shown to be robust on this dataset. Our approach was thus able to discover understandable subgroups from observational data that have predictive value and which may be considered by the clinical users to get insight into who responds positively or negatively to a proposed treatment.

UNASSIGNED: : Nabiximols oromucosal spray,a cannabis-based medicine containing a balanced ratio of Δ-9-tetrahydrocannabinol and cannabidiol, is approved widely as an add-on therapy for symptomatic relief of spasticity in people with multiple sclerosis (MS). Most safety data for nabiximols derive from use in MS spasticity, with some data available from the analgesia area.
UNASSIGNED: : This review compiles safety and tolerability data from all published observational studies, registry analyses, and case reports identified in systematic searches in which nabiximols oromucosal spray was investigated for spasticity (n = 20) and/or chronic non-cancer pain (n = 4). Aligning with the known safety profile of nabiximols as demonstrated in randomized controlled trials, common adverse events reported consistently across studies conducted under clinical practice conditions were dizziness, fatigue and somnolence. The serious adverse event (SAE) rate with nabiximols in MS spasticityobservational studies was 3.1% (137/4351). A total of 39 treatment-related SAEs were reported in 32 patients with spasticity, all of which (where specified) were resolved. No treatment-related SAEs were recorded in nabiximols pain studies.
UNASSIGNED: : Real-world experience with nabiximols oromucosal spray in treating spasticity and chronic pain indicates that, overall, it is well tolerated and has a good safety profile.

To demonstrate the application of the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) principles described in our companion article to hypertension treatments and assess internal and external validity of the generated evidence.
LEGEND defines a process for high-quality observational research based on 10 guiding principles. We demonstrate how this process, here implemented through large-scale propensity score modeling, negative and positive control questions, empirical calibration, and full transparency, can be applied to compare antihypertensive drug therapies. We assess internal validity through covariate balance, confidence-interval coverage, between-database heterogeneity, and transitivity of results. We assess external validity through comparison to direct meta-analyses of randomized controlled trials (RCTs).
From 21.6 million unique antihypertensive new users, we generate 6 076 775 effect size estimates for 699 872 research questions on 12 946 treatment comparisons. Through propensity score matching, we achieve balance on all baseline patient characteristics for 75% of estimates, observe 95.7% coverage in our effect-estimate 95% confidence intervals, find high between-database consistency, and achieve transitivity in 84.8% of triplet hypotheses. Compared with meta-analyses of RCTs, our results are consistent with 28 of 30 comparisons while providing narrower confidence intervals.
We find that these LEGEND results show high internal validity and are congruent with meta-analyses of RCTs. For these reasons we believe that evidence generated by LEGEND is of high quality and can inform medical decision-making where evidence is currently lacking. Subsequent publications will explore the clinical interpretations of this evidence.

Providing terminally ill patients with access to experimental treatments, as allowed by recent \"right to try\" laws and \"expanded access\" programs, poses a variety of ethical questions. While practitioners and investigators may assume it is impossible to learn the effects of these treatment without randomized trials, this paper describes a simple tool to estimate the effects of these experimental treatments on those who take them, despite the problem of selection into treatment, and without assumptions about the selection process. The key assumption is that the average outcome, such as survival, would remain stable over time in the absence of the new treatment. Such an assumption is unprovable, but can often be credibly judged by reference to historical data and by experts familiar with the disease and its treatment. Further, where this assumption may be violated, the result can be adjusted to account for a hypothesized change in the non-treatment outcome, or to conduct a sensitivity analysis. The method is simple to understand and implement, requiring just four numbers to form a point estimate. Such an approach can be used not only to learn which experimental treatments are promising, but also to warn us when treatments are actually harmful - especially when they might otherwise appear to be beneficial, as illustrated by example here. While this note focuses on experimental medical treatments as a motivating case, more generally this approach can be employed where a new treatment becomes available or has a large increase in uptake, where selection bias is a concern, and where an assumption on the change in average non-treatment outcome over time can credibly be imposed.

For a typical medical research project based on observational data, sequential routine analyses are often essential to comprehend the data on hand and to draw valid conclusions.  However, generating reports in SAS ® for routine analyses can be a time-consuming and tedious process, especially when dealing with large databases with a massive number of variables in an iterative and collaborative research environment. In this work, we present a general workflow of research based on an observational database and a series of SAS ® macros that fits this framework, which covers a streamlined data analyses and produces journal-quality summary tables. The system is generic enough to fit a variety of research projects and enables researchers to build a highly organized and concise coding for quick updates as research evolves. The result reports promote communication in collaborations and will escort the research with ease and efficiency.